Intent based search result interaction

ABSTRACT

A method and system for improving an intent based search is provided. The method includes analyzing a search phase entered by a user with respect to a Website level search query for specified subject matter. In response, a subject based intent classification is determined to be associated with a confidence factor of less than 100 percent confidence with respect to the subject based intent classification being correct. The subject based intent classification is compared to search results data and a subset of search results correlating to the subject based intent classification is determined and presented to the user. An interaction of the user with respect to the subset of search results is determined and in response, a value of the confidence factor is derived.

This application is a continuation application claiming priority to Ser.No. 15/279,621 filed Sep. 29, 2016, now U.S. Pat. No. 9,785,717, issuedOct. 10, 2017.

FIELD

The present invention relates generally to a method implementing anintent based search query and in particular to a method and associatedsystem for improving computer search query technology by correlating auser query intent with Internet search results and determining anassociated confidence factor.

BACKGROUND

Accurately predicting search parameters based on input from a usertypically includes an inaccurate process with little flexibility.Analyzing current search parameters with respect various attributes mayinclude a complicated process that may be time consuming and require alarge amount of resources. Accordingly, there exists a need in the artto overcome at least some of the deficiencies and limitations describedherein above.

SUMMARY

A first aspect of the invention provides a intent based searchimprovement method comprising: analyzing, by a processor a hardwaredevice enabling a natural language classifier (NLC) circuit of thehardware device, a search phase entered by a user in a search field of agraphical user interface with respect to a Website level search queryfor specified subject matter; determining, by the processor executingthe NLC circuit with respect to results of the analyzing, at least onesubject based intent classification associated with the search query;determining, by the processor executing the NLC circuit, that the atleast one subject based intent classification comprises a confidencefactor of less than 100 percent confidence with respect to the at leastone subject based intent classification being correct; comparing, by theprocessor, the at least one subject based intent classification tosearch results data of a search results data repository; generating, bythe processor based on results of the determining that the at least onesubject based intent classification comprises a confidence factor ofless than 100 percent confidence and the comparing, a subset of searchresults of the search results data, wherein the subset of search resultscorrelates to the at least one subject based intent classification;presenting, by the processor, the at least one subject based intentclassification and the subset of search results; determining, by theprocessor, an interaction of the user with respect to the subset ofsearch results; and determining, by the processor in response to thedetermining that the user interacts with the subset of search results, avalue of the confidence factor.

A second aspect of the invention provides a computer program product,comprising a computer readable hardware storage device storing acomputer readable program code, the computer readable program codecomprising an algorithm that when executed by a processor of a hardwaredevice implements an intent based search improvement method, the methodcomprising: analyzing, by the processor enabling a natural languageclassifier (NLC) circuit of the hardware device, a search phase enteredby a user in a search field of a graphical user interface with respectto a Website level search query for specified subject matter;determining, by the processor executing the NLC circuit with respect toresults of the analyzing, at least one subject based intentclassification associated with the search query; determining, by theprocessor executing the NLC circuit, that the at least one subject basedintent classification comprises a confidence factor of less than 100percent confidence with respect to the at least one subject based intentclassification being correct; comparing, by the processor, the at leastone subject based intent classification to search results data of asearch results data repository; generating, by the processor based onresults of the determining that the at least one subject based intentclassification comprises a confidence factor of less than 100 percentconfidence and the comparing, a subset of search results of the searchresults data, wherein the subset of search results correlates to the atleast one subject based intent classification; presenting, by theprocessor, the at least one subject based intent classification and thesubset of search results; determining, by the processor, an interactionof the user with respect to the subset of search results; anddetermining, by the processor in response to the determining that theuser interacts with the subset of search results, a value of theconfidence factor.

A third aspect of the invention provides a hardware device comprising aprocessor coupled to a computer-readable memory unit, the memory unitcomprising instructions that when executed by the processor executes anintent based search improvement method comprising: analyzing, by theprocessor enabling a natural language classifier (NLC) circuit of thehardware device, a search phase entered by a user in a search field of agraphical user interface with respect to a Website level search queryfor specified subject matter; determining, by the processor executingthe NLC circuit with respect to results of the analyzing, at least onesubject based intent classification associated with the search query;determining, by the processor executing the NLC circuit, that the atleast one subject based intent classification comprises a confidencefactor of less than 100 percent confidence with respect to the at leastone subject based intent classification being correct; comparing, by theprocessor, the at least one subject based intent classification tosearch results data of a search results data repository; generating, bythe processor based on results of the determining that the at least onesubject based intent classification comprises a confidence factor ofless than 100 percent confidence and the comparing, a subset of searchresults of the search results data, wherein the subset of search resultscorrelates to the at least one subject based intent classification;presenting, by the processor, the at least one subject based intentclassification and the subset of search results; determining, by theprocessor, an interaction of the user with respect to the subset ofsearch results; and determining, by the processor in response to thedetermining that the user interacts with the subset of search results, avalue of the confidence factor.

The present invention advantageously provides a simple method andassociated system capable of accurately predicting search parameters.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for improving computer search querytechnology by correlating user query intent with Internet search resultsand determining an associated confidence factor, in accordance withembodiments of the present invention.

FIG. 2 illustrates an algorithm detailing a process flow enabled by thesystem of FIG. 1 for improving computer search query technology bycorrelating user query intent with Internet search results anddetermining an associated confidence factor, in accordance withembodiments of the present invention.

FIG. 3 illustrates a screen shot of a user interface enabled by thesystem of FIG. 1 for improving computer search query technology bycorrelating user query intent with Internet search results anddetermining an associated confidence factor, in accordance withembodiments of the present invention.

FIG. 4 illustrates a computer system used by the system of FIG. 1 forenabling a process for improving computer search query technology bycorrelating user query intent with Internet search results anddetermining an associated confidence factor, in accordance withembodiments of the present invention.

DETAILED DESCRIPTION

FIG. 1 illustrates a system 100 for improving computer search querytechnology by correlating user query intent with Internet search resultsand determining an associated confidence factor, in accordance withembodiments of the present invention. System 100 enables a process forproviding search results for a user query based on a correlation betweensearch results and user query intent. The user query is received viaWebsite and a type of intent associated with the user query isidentified via execution of a natural language processing analysis. Atype of intent indicates information being sought by the user query. Inresponse, a subset of the search results correlating to a type of queryintent above a specified threshold is presented to a user. Intent isdefined herein as an aim or purpose with respect to a subject associatedwith a user search query. The correlation process is executed byanalyzing indicators associated with an intent of a search query andpredicting associated search results. The analysis includes enabling anatural language classifier (NLC) circuit 19 to execute a semanticsearch and generate an intent domain associated with a subject basedintent classification and execute an unstructured data analysis processwith respect to a content corpus associated with the subject basedintent classification. A semantic search improves a search accuracy byunderstanding a user's intent in combination with a contextual meaningof terms as they appear within a searchable dataspace (e.g., theInternet, a closed system, etc.) to generate more relevant results.Semantic search systems consider various points including context ofsearch, location, intent, variation of words, synonyms, generalized andspecialized queries, concept matching, natural language queries, etc. toprovide relevant search results. A subset of search results associatedwith the subject based intent classification is generated and ranked.NLC circuit 19 applies deep learning techniques for predicting “best”predefined classes or categories associated with short input sentencesor phrases. The classes or categories may trigger a corresponding actionwith respect to an application such as, inter alia, directing a requestto a location or person, answering a question, etc. After the deeplearning techniques have completed execution, NLC circuit 19 returnsinformation associated with unknown text and a response may include thename for top classes and confidence values.

System 100 of FIG. 1 includes hardware devices 114 a . . . 114 n and anintent data repository 29 in communication with a hardware apparatus 14via a network 118. Hardware devices 114 a . . . 114 n and hardwareapparatus 14 each may comprise an embedded computer. An embeddedcomputer is defined herein as a remotely portable dedicated computercomprising a combination of computer hardware and software (fixed incapability or programmable) specifically designed for executing aspecialized function. Programmable embedded computers may comprisespecialized programming interfaces. Additionally, hardware devices 114 a. . . 114 n and hardware apparatus 14 may each comprise a specializedhardware device comprising specialized (non-generic) hardware andcircuitry (i.e., specialized discrete non-generic analog, digital, andlogic based circuitry) for executing a process described with respect toFIGS. 1-3. The specialized discrete non-generic analog, digital, andlogic based circuitry may include proprietary specially designedcomponents (e.g., a specialized integrated circuit such as a naturallanguage classifier (NLC) circuit 19 (as described, infra) designed foronly implementing an automated process for improving computer searchquery technology by correlating user query intent with Internet searchresults and determining an associated confidence factor. Hardwareapparatus 14 includes a memory system 8, software 17, and NLC circuit19. The memory system 8 (e.g., a database) and intent data repository 29may each include a single memory system. Alternatively, the memorysystem 8 and intent data repository 29 may each include a plurality ofmemory systems. Hardware devices 114 a . . . 114 n may comprise any typeof hardware devices (comprising embedded circuitry for only performingan automated process for improving computer search query technology bycorrelating user query intent with Internet search results anddetermining an associated confidence factor) including, inter alia, asmart phone, a PDA, a tablet computer, a laptop computer, etc.

System 100 of FIG. 1 enables a process for determining a query basedintent associated with a search query as follows:

During a process for enabling a natural language search (initiated by auser) at a Website, an application programming interface (API) forderiving an intent of the user (e.g., via natural language classifiers(NLC)) is applied with respect to a natural language search phraseentered in a search application GUI. For example, the intent may bedetermined with respect to the natural language search phrase beingdirected toward products, support, or content classifications, asdefined within a ground truth (i.e., information provided by directobservation) for the Website. If an intent is not determined via thenatural language search, a standard set of relevant options (i.e., withrespect to past searches) retrieved from a result repository may bereturned. If an intent is determined via the natural language search, atailored result is generated based on an intent classification(s) and astandard set of results from a result repository comprising results fromprevious search queries. The tailored result is subsequently presentedto the user via a specialized circuit and GUI. For example, if a userwishes to locate help with respect to repairing a bicycle tire andbegins to type the phrase “How do I fix a bike”, a natural languageclassification process is continuously executed with respect to thesearch query resulting in an intent of “support” being determined to beclosely correlated within a ground truth for the Website. The intent of“support” is determined because the natural language classifier'scognitive matching capabilities resulted in completed matches (withrespect to a high confidence value) with respect to all possibledetected patterns in the natural language search query. Examples of thedetected patterns may include, inter alia, the following phrases: “howdo I”, “I fix”, “a bike”, etc. such that all lend of the aforementioneddetected patterns provide evidence with respect to the detected intentof “support”. Furthermore, many additional intent phrases such as“cycling” or “consumables” could have been detected based on thedetected patterns. A standard set of results may be generated if noviable intent(s) is detected via execution of the natural languagesearch query thereby yielding a standard set of results. As more intentphrases are inferred from the query, additional context associated witha subset of results more relevant to the user is received. Therefore,system 100 refines results (with respect to intent) based on thespecific intent(s) of the user thereby yielding a tailored list resultsfor the entered search query provided to the user. For example (in thisinstance), a selection entitled “How do I patch a bicycle tire?” enablessystem 100 to respond with alternative word or phrases (differing fromoriginally entered text) such that an actual intent phrase isdetermined.

System 100 of FIG. 1 additionally provides a ground truth includingWebsite provided resources classified by products, support and contentitems. Each of the products, support and content items are tagged forrelevancy such that when a user enters a natural language search via theWebsite, NLCs are applied to determine an intent associated with anatural language search phrase within a search application. In response,a result comprising a corresponding intent classification and associatedconfidence levels are returned. If an intent is unable to be determinedfrom the search, standard search results are returned. If an intent isable to be determined from the user's search, a retrieve and rankprocess is executed via usage of the Website resources/classifiers,associated relevancy tags, and intent classification with associatedconfidence levels for producing a superior ranked search result. Forexample (in this instance), if a user enters a search for “How do I fixa flat tire” and the intent is determined to closely correlate with the“support” category in the ground truth, the search results returned tothe user will comprise a ranked result provided by items within the“support” category.

System 100 of FIG. 1 executes a process such when a ranked search resultis presented to a user with respect to a determined user's intent (i.e.,the search results are biased such that a ground truth classificationdetermined to best match the user's search results is featured mostprominently), the user may select a search result not comprising apreferred classification. In response, a mismatch with respect to intentis flagged, reported, and potentially corrected within the NLC.Additionally, an intent of search terms (product, support, content,etc.) of a query may be determined and associated results may bepresented to a user based on the determined intent. For example, ifthree top search results for the phrase “replacement tire tubes” arepresented to the user and in response, the user clicks (e.g., via amouse) on tire tubes for purchase instead of watching a video withrespect to fixing a flat tire, a classifier may be re-classified ormarked classifier as incorrect as a product should have been selectedinstead of content based on X number of people behaving similarly.

FIG. 2 illustrates an algorithm detailing a process flow enabled bysystem 100 of FIG. 1 for improving computer search query technology bycorrelating user query intent with Internet search results anddetermining an associated confidence factor, in accordance withembodiments of the present invention. Each of the steps in the algorithmof FIG. 2 may be enabled and executed in any order by a computerprocessor(s) or any type of specialized hardware executing specializedcomputer code. In step 200, a search phase entered by a user in a searchfield of a graphical user interface with respect to a Website levelsearch query for specified subject matter is analyzed by an NLC circuitof a hardware device. The query may comprise a specified Website networkquery. In step 202, a subject based intent classification(s) associatedwith the search query is determined based on the analysis of step 200.In step 204, it is determined that the subject based intentclassification comprises a confidence factor of less than 100 percentconfidence with respect to the subject based intent classification beingcorrect. In step 210, the subject based intent classification iscompared to search results data of a search results data repository. Instep 212, a subset of search results of the search results data isgenerated based on the results of steps 204 and 210. The subsetcorrelates to the subject based intent classification. In step 214, thesubject based intent classification and the subset of search results arepresented to the user. In step 216, an interaction of the user withrespect to the subset of search results is determined. The interactionmay include, inter alia, a mouse activated click through action, apurchase made by the user with respect to an item related to the subsetof search results, a wish list addition action, a favorite list additionaction, a share action, a duration spent by the user on the subset ofsearch results a number of accesses by the user with respect to subsetof search results, etc. In step 218, a value of the confidence factor isdetermined based on the results of step 216. In step 220, a confidencethreshold comprising a value less than the value of the confidencefactor is determined. In step 224, the confidence factor is classifiedwith respect to future searches associated with the subject based intentclassification. In step 226, steps 200-224 are repeated to determinealternative search results.

FIG. 3 illustrates a screen shot of a user interface 300 enabled bysystem 100 of FIG. 1 for improving computer search query technology bycorrelating user query intent with Internet search results anddetermining an associated confidence factor, in accordance withembodiments of the present invention. User interface 300 comprises aninput field 302 comprising a search query input for the phrase “how tofix a flat”. In response, system 100 generates and presents a top intentconfidence of 95% with respect to the phrase “how to fix a flat”. Inaccordance with a result set matching the top intents (e.g., resultsrelated to research 309 and buy now options 317, the user interacts withmore of the “buy now” results than the “research” results. Therefore,system 100 provides a low confidence with respect to the search querybeing associated with “research” based on the user interaction.Therefore, the next time a same or similar query is entered, aconfidence score may be adjusted as per results 320 to 90%.

FIG. 4 illustrates a computer system 90 (e.g., hardware devices 114 a .. . 114 n and hardware apparatus 14) used by or comprised by the systemof FIG. 1 for improving computer search query technology by correlatinguser query intent with Internet search results and determining anassociated confidence factor, in accordance with embodiments of thepresent invention.

Aspects of the present invention may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, microcode, etc.) or an embodiment combiningsoftware and hardware aspects that may all generally be referred toherein as a “circuit,” “module,” or “system.”

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing apparatus receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, device(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing device to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing device, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing device, and/or other devicesto function in a particular manner, such that the computer readablestorage medium having instructions stored therein comprises an articleof manufacture including instructions which implement aspects of thefunction/act specified in the flowchart and/or block diagram block orblocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing device, or other device tocause a series of operational steps to be performed on the computer,other programmable device or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable device, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The computer system 90 illustrated in FIG. 4 includes a processor 91, aninput device 92 coupled to the processor 91, an output device 93 coupledto the processor 91, and memory devices 94 and 95 each coupled to theprocessor 91. The input device 92 may be, inter alia, a keyboard, amouse, a camera, a touchscreen, etc. The output device 93 may be, interalia, a printer, a plotter, a computer screen, a magnetic tape, aremovable hard disk, a floppy disk, etc. The memory devices 94 and 95may be, inter alia, a hard disk, a floppy disk, a magnetic tape, anoptical storage such as a compact disc (CD) or a digital video disc(DVD), a dynamic random access memory (DRAM), a read-only memory (ROM),etc. The memory device 95 includes a computer code 97. The computer code97 includes algorithms (e.g., the algorithm of FIG. 2) for enabling aprocess for improving computer search query technology by correlatinguser query intent with Internet search results and determining anassociated confidence factor. The processor 91 executes the computercode 97. The memory device 94 includes input data 96. The input data 96includes input required by the computer code 97. The output device 93displays output from the computer code 97. Either or both memory devices94 and 95 (or one or more additional memory devices such as read onlymemory device 96) may include algorithms (e.g., the algorithm of FIG. 2)and may be used as a computer usable medium (or a computer readablemedium or a program storage device) having a computer readable programcode embodied therein and/or having other data stored therein, whereinthe computer readable program code includes the computer code 97.Generally, a computer program product (or, alternatively, an article ofmanufacture) of the computer system 90 may include the computer usablemedium (or the program storage device).

In some embodiments, rather than being stored and accessed from a harddrive, optical disc or other writeable, rewriteable, or removablehardware memory device 95, stored computer program code 84 (e.g.,including the algorithm of FIG. 2) may be stored on a static,nonremovable, read-only storage medium such as a Read-Only Memory (ROM)device 85, or may be accessed by processor 91 directly from such astatic, nonremovable, read-only medium 85. Similarly, in someembodiments, stored computer program code 97 may be stored ascomputer-readable firmware 85, or may be accessed by processor 91directly from such firmware 85, rather than from a more dynamic orremovable hardware data-storage device 95, such as a hard drive oroptical disc.

Still yet, any of the components of the present invention could becreated, integrated, hosted, maintained, deployed, managed, serviced,etc. by a service supplier who offers to enable a process for improvingcomputer search query technology by correlating user query intent withInternet search results and determining an associated confidence factor.Thus, the present invention discloses a process for deploying, creating,integrating, hosting, maintaining, and/or integrating computinginfrastructure, including integrating computer-readable code into thecomputer system 90, wherein the code in combination with the computersystem 90 is capable of performing a method for enabling a process forimproving computer search query technology by correlating user queryintent with Internet search results and determining an associatedconfidence factor. In another embodiment, the invention provides abusiness method that performs the process steps of the invention on asubscription, advertising, and/or fee basis. That is, a servicesupplier, such as a Solution Integrator, could offer to enable a processfor improving computer search query technology by correlating user queryintent with Internet search results and determining an associatedconfidence factor. In this case, the service supplier can create,maintain, support, etc. a computer infrastructure that performs theprocess steps of the invention for one or more customers. In return, theservice supplier can receive payment from the customer(s) under asubscription and/or fee agreement and/or the service supplier canreceive payment from the sale of advertising content to one or morethird parties.

While FIG. 4 shows the computer system 90 as a particular configurationof hardware and software, any configuration of hardware and software, aswould be known to a person of ordinary skill in the art, may be utilizedfor the purposes stated supra in conjunction with the particularcomputer system 90 of FIG. 4. For example, the memory devices 94 and 95may be portions of a single memory device rather than separate memorydevices.

While embodiments of the present invention have been described hereinfor purposes of illustration, many modifications and changes will becomeapparent to those skilled in the art. Accordingly, the appended claimsare intended to encompass all such modifications and changes as fallwithin the true spirit and scope of this invention.

What is claimed is:
 1. A search query technology improvement methodcomprising: analyzing, by a processor of an embedded hardware deviceenabling a natural language classifier (NLC) circuit of said embeddedhardware device, a search phrase entered by a user in a search field ofa graphical user interface with respect to a Website level search queryfor specified subject matter; determining, by said processor executingsaid NLC circuit, that at least one subject based intent classificationassociated with results of said analyzing and said search querycomprises a confidence factor of less than 100 percent confidence withrespect to said at least one subject based intent classification beingcorrect; detecting, by the NLC circuit via execution of an automatednatural language search query, all detected possible patterns withinsaid search query; matching, by the NLC circuit, all of the detectedpossible patterns with respect to said search phrase associated withsaid at least one subject based intent classification; executing, by theNLC circuit, a deep learning process for automated prediction of classesand categories for said search phrase; triggering, by the NLC circuit,an action with respect to software application execution; comparing, bysaid processor, said at least one subject based intent classification tosearch results data of a search results data repository; generating, bysaid processor based on results of said determining that said at leastone subject based intent classification comprises a confidence factor ofless than 100 percent confidence and said comparing, a subset of searchresults of said search results data, wherein said subset of searchresults correlates to said at least one subject based intentclassification; determining, by said processor in response todetermining that said user interacts with said subset of search results,a value of said confidence factor; executing, by said processor, asemantic search with respect to said value of said confidence factor;and generating, by said processor in response to said executing,relevant search results thereby improving a search accuracy for saidsearch query via determination of user intent in combination with acontextual meaning of semantic search terms appearing within asearchable dataspace.
 2. The method of claim 1, further comprising:determining, by said processor, a confidence threshold comprising avalue less than said value of said confidence factor; and classifying,by said processor based on said confidence threshold, said confidencefactor with respect to future searches associated with said at least onesubject based intent classification.
 3. The method of claim 1, furthercomprising: additionally analyzing, by a processor enabling said NLCcircuit, an additional search phase entered by said user in said searchfield of said graphical user interface with respect to an additionaldomain specific search query for additional specified subject matter;determining, by said processor executing said NLC circuit with respectto results of said additionally analyzing, an additional subject basedintent classification associated with said additional domain specificsearch query; comparing, by said processor, said confidence threshold toa confidence factor of said additional subject based intentclassification; determining, by said processor based on results of saidcomparing said confidence threshold, that said confidence factor of saidadditional subject based intent classification exceeds said confidencethreshold; generating, by said processor based on results of saiddetermining that said confidence factor of said additional subject basedintent classification exceeds said confidence threshold, an alternativesubset of search results of said search results data, wherein saidsubset of search results correlates to said additional subject basedintent classification; and presenting, by said processor, saidadditional subject based intent classification and said alternativesubset of search results.
 4. The method of claim 1, further comprising:presenting, by said processor in response to said determining saidinteraction of said user, additional suggested search results based onsaid at least one subject based intent classification.
 5. The method ofclaim 1, wherein said query is associated with a specified domainspecific network.
 6. The method of claim 1, wherein said interactioncomprises a mouse activated click through action.
 7. The method of claim1, wherein said interaction comprises a purchase made by said user withrespect to an item related to the subset of search results.
 8. Themethod of claim 1, wherein said interaction comprises a wish listaddition action.
 9. The method of claim 1, wherein said interactioncomprises a favorite list addition action.
 10. The method of claim 1,wherein said interaction comprises a share action.
 11. The method ofclaim 1, wherein said interaction comprises a duration spent by the useron said subset of search results.
 12. The method of claim 1, whereinsaid interaction comprises a number of accesses by said user withrespect to said subset of search results.
 13. The method of claim 1,further comprising: providing at least one support service for at leastone of creating, integrating, hosting, maintaining, and deployingcomputer-readable code in the hardware device, said code being executedby the computer processor to implement: said analyzing, said determiningthat said at least one subject based intent classification comprisessaid confidence factor of less than 100 percent confidence with respectto said at least one subject based intent classification being correct,said comparing, said generating, said interaction, and said determiningsaid value.
 14. A computer program product, comprising a computerreadable hardware storage device storing a computer readable programcode, said computer readable program code comprising an algorithm thatwhen executed by a processor of an embedded hardware device implements asearch query technology improvement method, said method comprising:analyzing, by said processor enabling a natural language classifier(NLC) circuit of said embedded hardware device, a search phrase enteredby a user in a search field of a graphical user interface with respectto a Website level search query for specified subject matter;determining, by said processor executing said NLC circuit, that at leastone subject based intent classification associated with results of saidanalyzing and said search query comprises a confidence factor of lessthan 100 percent confidence with respect to said at least one subjectbased intent classification being correct; detecting, by the NLC circuitvia execution of an automated natural language search query, alldetected possible patterns within said search query; matching, by theNLC circuit, all of the detected possible patterns with respect to saidsearch phrase associated with said at least one subject based intentclassification; executing, by the NLC circuit, a deep learning processfor automated prediction of classes and categories for said searchphrase; triggering, by the NLC circuit, an action with respect tosoftware application execution; comparing, by said processor, said atleast one subject based intent classification to search results data ofa search results data repository; generating, by said processor based onresults of said determining that said at least one subject based intentclassification comprises a confidence factor of less than 100 percentconfidence and said comparing, a subset of search results of said searchresults data, wherein said subset of search results correlates to saidat least one subject based intent classification; determining, by saidprocessor in response to determining that said user interacts with saidsubset of search results, a value of said confidence factor; executing,by said processor, a semantic search with respect to said value of saidconfidence factor; and generating, by said processor in response to saidexecuting, relevant search results thereby improving a search accuracyfor said search query via determination of user intent in combinationwith a contextual meaning of semantic search terms appearing within asearchable dataspace.
 15. The computer program product of claim 14,wherein said method further comprises: determining, by said processor, aconfidence threshold comprising a value less than said value of saidconfidence factor; and classifying, by said processor based on saidconfidence threshold, said confidence factor with respect to futuresearches associated with said at least one subject based intentclassification.
 16. The computer program product of claim 14, whereinsaid method further comprises: additionally analyzing, by a processorenabling said NLC circuit, an additional search phase entered by saiduser in said search field of said graphical user interface with respectto an additional domain specific search query for additional specifiedsubject matter; determining, by said processor executing said NLCcircuit with respect to results of said additionally analyzing, anadditional subject based intent classification associated with saidadditional domain specific search query; comparing, by said processor,said confidence threshold to a confidence factor of said additionalsubject based intent classification; determining, by said processorbased on results of said comparing said confidence threshold, that saidconfidence factor of said additional subject based intent classificationexceeds said confidence threshold; generating, by said processor basedon results of said determining that said confidence factor of saidadditional subject based intent classification exceeds said confidencethreshold, an alternative subset of search results of said searchresults data, wherein said subset of search results correlates to saidadditional subject based intent classification; and presenting, by saidprocessor, said additional subject based intent classification and saidalternative subset of search results.
 17. The computer program productof claim 14, wherein said method further comprises: presenting, by saidprocessor in response to said determining said interaction of said user,additional suggested search results based on said at least one subjectbased intent classification.
 18. The computer program product of claim14, wherein said query is associated with a specified Website network.19. The computer program product of claim 14, wherein said interactioncomprises a mouse activated click through action.
 20. An embeddedhardware device comprising a processor coupled to a computer-readablememory unit, said memory unit comprising instructions that when executedby the processor executes a search query technology improvement methodcomprising: analyzing, by said processor enabling a natural languageclassifier (NLC) circuit of said embedded hardware device, a searchphrase entered by a user in a search field of a graphical user interfacewith respect to a Website level search query for specified subjectmatter; determining, by said processor executing said NLC circuit, thatat least one subject based intent classification associated with resultsof said analyzing and said search query comprises a confidence factor ofless than 100 percent confidence with respect to said at least onesubject based intent classification being correct; detecting, by the NLCcircuit via execution of an automated natural language search query, alldetected possible patterns within said search query; matching, by theNLC circuit, all of the detected possible patterns with respect to saidsearch phrase associated with said at least one subject based intentclassification; executing, by the NLC circuit, a deep learning processfor automated prediction of classes and categories for said searchphrase; triggering, by the NLC circuit, an action with respect tosoftware application execution; comparing, by said processor, said atleast one subject based intent classification to search results data ofa search results data repository; generating, by said processor based onresults of said determining that said at least one subject based intentclassification comprises a confidence factor of less than 100 percentconfidence and said comparing, a subset of search results of said searchresults data, wherein said subset of search results correlates to saidat least one subject based intent classification; determining, by saidprocessor in response to determining that said user interacts with saidsubset of search results, a value of said confidence factor; executing,by said processor, a semantic search with respect to said value of saidconfidence factor; and generating, by said processor in response to saidexecuting, relevant search results thereby improving a search accuracyfor said search query via determination of user intent in combinationwith a contextual meaning of semantic search terms appearing within asearchable dataspace.